Workflow Templates

This workflow helps marketers verify and update data using EffiBotics Email Verifier API. Copy and create a list with emails as on this one https://docs.google.com/spreadsheets/d/1rzuojNGTaBvaUEON6cakQRDva3ueGg5kNu9v12aaSP4/edit#gid=0 The trigger checks for any updates in the number of rows that are present in a sheet and updates the verified emails on Google sheets Once you update a new cell, the new data is read, and the email is checked for its validity before. The results are then updated in real-time on the sheet. Happy Emailing!

Telegram AI Assistant: Summarize Links & Generate Images On Demand This workflow turns any Telegram chat into a smart assistant. By typing simple commands like /summary or /img, users can trigger powerful AI actions—directly from Telegram. ✨ What It Does This automation listens for specific commands in Telegram messages: /help: Sends a help menu explaining available commands. /summary <link>: Fetches a webpage, extracts its content, and summarizes it using OpenAI into 10–12 bullet points. /img <prompt>: Sends the image prompt to OpenAI and replies that the request has been received (designed for future integration with image APIs). 📦 Features ✅ Works instantly in Telegram 🧠 Uses OpenAI for text summarization and image prompt processing 🌐 Scrapes and cleans raw article text before summarizing 📤 Replies directly to the same Telegram thread 🔧 Easily expandable to support more commands 🔧 Use Cases Research Summaries**: Quickly condense articles or reports shared in chat. Content Review**: Get team-friendly TL;DRs of long blog posts or product pages. Creative Brainstorming**: Share visual ideas via /img and get quick prompts logged. Customer Support**: Offer instant answers in group chats (with further extension). Daily Digest Bot**: Connect to news feeds and auto-summarize updates. 🚀 Getting Started Clone this workflow and connect your Telegram Bot. Insert your OpenAI credentials. Deploy and test by messaging /summary https://example.com in your Telegram group or DM. Expand with new commands or connect Stability.ai or other services for real image generation. 🔗 Author & Resources Built by Yaron Been Follow more automations at nofluff.online

This n8n workflow allows you to reset all user roles in Zammad to specified default roles. It ensures consistency in role management across your Zammad instance. Features Retrieve all active users from Zammad. Update each user's roles to predefined default role IDs. Exclude specific users by their IDs from the update process. Simple configuration for default roles and excluded users. Usage Import the Workflow: Upload the provided .json file into your n8n instance. Configure Variables: zammad_base_url: Your Zammad instance URL. zammad_api_key: Your Zammad API key. default_roles: List of default role IDs to apply to all users. exclude_zammad_users_by_id: List of user IDs to exclude from the update. Run the Workflow: Execute the workflow to update roles automatically. Issues and Suggestions For issues or suggestions, visit the GitHub Repository.

This workflow demonstrates how to enrich data from a list of companies in a spreadsheet. While this workflow is production-ready if all steps are followed, adding error handling would enhance its robustness. Important notes Check legal regulations**: This workflow involves scraping, so make sure to check the legal regulations around scraping in your country before getting started. Better safe than sorry! Mind those tokens**: OpenAI tokens can add up fast, so keep an eye on usage unless you want a surprising bill that could knock your socks off! 💸 Main Workflow Node 1 - Webhook This node triggers the workflow via a webhook call. You can replace it with any other trigger of your choice, such as form submission, a new row added in Google Sheets, or a manual trigger. Node 2 - Get Rows from Google Sheet This node retrieves the list of companies from your spreadsheet. here is the Google Sheet Template you can use. The columns in this Google Sheet are: Company**: The name of the company Website**: The website URL of the company These two fields are required at this step. Business Area**: The business area deduced by OpenAI from the scraped data Offer**: The offer deduced by OpenAI from the scraped data Value Proposition**: The value proposition deduced by OpenAI from the scraped data Business Model**: The business model deduced by OpenAI from the scraped data ICP**: The Ideal Customer Profile deduced by OpenAI from the scraped data Additional Information**: Information related to the scraped data, including: Information Sufficiency: Description: Indicates if the information was sufficient to provide a full analysis. Options: "Sufficient" or "Insufficient" Insufficient Details: Description: If labeled "Insufficient," specifies what information was missing or needed to complete the analysis. Mismatched Content: Description: Indicates whether the page content aligns with that of a typical company page. Suggested Actions: Description: Provides recommendations if the page content is insufficient or mismatched, such as verifying the URL or searching for alternative sources. Node 3 - Loop Over Items This node ensures that, in subsequent steps, the website in "extra workflow input" corresponds to the row being processed. You can delete this node, but you'll need to ensure that the "query" sent to the scraping workflow corresponds to the website of the specific company being scraped (rather than just the first row). Node 4 - AI Agent This AI agent is configured with a prompt to extract data from the content it receives. The node has three sub-nodes: OpenAI Chat Model: The model used is currently gpt4-o-mini. Call n8n Workflow: This sub-node calls the workflow to use ScrapingBee and retrieves the scraped data. Structured Output Parser: This parser structures the output for clarity and ease of use, and then adds rows to the Google Sheet. Node 5 - Update Company Row in Google Sheet This node updates the specific company's row in Google Sheets with the enriched data. Scraper Agent Workflow Node 1 - Tool Called from Agent This is the trigger for when the AI Agent calls the Scraper. A query is sent with: Company name Website (the URL of the website) Node 2 - Set Company URL This node renames a field, which may seem trivial but is useful for performing transformations on data received from the AI Agent. Node 3 - ScrapingBee: Scrape Company's Website This node scrapes data from the URL provided using ScrapingBee. You can use any scraper of your choice, but ScrapingBee is recommended, as it allows you to configure scraper behavior directly. Once configured, copy the provided "curl" command and import it into n8n. Node 4 - HTML to Markdown This node converts the scraped HTML data to Markdown, which is then sent to OpenAI. The Markdown format generally uses fewer tokens than HTML. Improving the Workflow It's always a pleasure to share workflows, but creators sometimes want to keep some magic to themselves ✨. Here are some ways you can enhance this workflow: Handle potential errors Configure the scraper tool to scrape other pages on the website. Although this will cost more tokens, it can be useful (e.g., scraping "Pricing" or "About Us" pages in addition to the homepage). Instead of Google Sheets, connect directly to your CRM to enrich company data. Trigger the workflow from form submissions on your website and send the scraped data about the lead to a Slack or Teams channel.

This workflow allows you to recognize a folder with receipts or invoices (make sure your files are in .pdf, .png, or .jpg format). The workflow can be triggered via the "Test workflow" button, and it also monitors the folder for new files, automatically recognizing them. Video Demo https://youtu.be/mGPt7fqGQD8 1. n8n import glitch After import, the trigger node "When clicking 'Test workflow'" might be disconnected. You need to connect it via 2 arrows to "Google Sheets1" and "Google Drive" nodes. So, the workflow has 2 triggers - via button, and via Google Sheets "new file" event - both of these triggers should be connected to 2 nodes. Here is how it should look like: https://ocr.oakpdf.com/n8n_fix.png 2. Set up RapidAPI HTTP auth key Create new "HTTP header" n8n credential and paste your RapidAPI key from https://rapidapi.com/restyler/api/receipt-and-invoice-ocr-api into it. https://ocr.oakpdf.com/n8n_api_key.png Make sure "HTTP Request" node uses this credential. 3. Set up your Google Auth You need a Google connection to work with your Google Sheets and Google Drive accounts: https://docs.n8n.io/integrations/builtin/credentials/google/oauth-generic/#finish-your-n8n-credential 4. Set up Google Sheets Copy this Google Sheets document: https://docs.google.com/spreadsheets/d/1G0w-OMdFRrtvzOLPpfFJpsBVNqJ9cfRLMKCVWfrTQBg/edit?usp=sharing Custom document formats and advanced usage Email: contact@scrapeninja.net Linkedin: https://www.linkedin.com/in/anthony-sidashin/

Google Veo 3 Video Generator Description Sound on: Google’s flagship Veo 3 text to video model, with audio Overview This n8n workflow integrates with the Replicate API to use the google/veo-3 model. This powerful AI model can generate high-quality video content based on your inputs. Features Easy integration with Replicate API Automated status checking and result retrieval Support for all model parameters Error handling and retry logic Clean output formatting Parameters Required Parameters prompt** (string): Text prompt for video generation Optional Parameters seed** (integer, default: None): Random seed. Omit for random generations resolution** (string, default: 720p): Resolution of the generated video negative_prompt** (string, default: None): Description of what to discourage in the generated video How to Use Set up your Replicate API key in the workflow Configure the required parameters for your use case Run the workflow to generate video content Access the generated output from the final node API Reference Model: google/veo-3 API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of video generation parameters

Who is this for? For developers and organizations that need to convert HTML files to PDF. What problem is this workflow solving? The file format conversion problem. What this workflow does Converts HTML to file. Converts the HTML file to PDF. Stores the PDF file in the local file system. How to customize this workflow to your needs Open the HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Optionally, additional Body Parameters can be added for the converter.

contract input: length of the strings and number of copies output: random strings as specified. randomness determined by Crypto node (generate/base64) How to run the workflow Import the workflow into your n8n project Click the Form Trigger to specify the length of the strings and how many copies to generate The workflow runs then displays the generated random strings How to set up No additional set up is necessary to execute the workflow manually. integration Patterns of interests formTrigger node to submit a form, then use form (end) node to display results at the end of the triggered workflow. set(dup)-summarize(concatenate) to run a part of the workflow multiple times then merge the results to one piece of data

Quick overview This workflow receives an AOG parts request via webhook, validates OEM eligibility, life history, and airworthiness directives via external APIs, and uses OpenAI to generate decision rationales. It then reserves eligible parts or quarantines blocked parts, archives the packet to Google Drive, and sends Telegram notifications. How it works Receives a POST webhook request containing aircraft context and an array of requested serialized parts. Normalizes the incoming payload and processes each part individually. Calls external OEM and MRO/authority HTTP APIs to fetch OEM eligibility, serialized life history, and airworthiness directive status for each part. Combines the evidence and applies deterministic rules to flag release blockers (for example missing release certificate, expired life, OEM ineligible, or open directives) and set the part’s release state. Uses OpenAI (Chat Completions via LangChain) to generate a strict JSON explanation of the decision, including rationale, missing evidence, and follow-up maintenance questions. If the part is eligible, creates an AOG reservation in the MRO system; otherwise, creates a quarantine record in the MRO system. Aggregates all per-part decisions into a single release packet, archives it as a JSON file to Google Drive, and sends the packet details to a Telegram chat. Setup Provide an HTTP Header Auth credential for the external OEM/MRO/authority APIs and replace the placeholder example endpoints with your real API base URLs. Add an OpenAI API credential and confirm the selected model (gpt-5-mini) is available for your account. Add Google Drive OAuth credentials and replace REPLACE_FOLDER_ID with the target folder ID for archiving the release packet. Add a Telegram Bot credential and replace REPLACE_CHAT_ID with the chat/channel ID that should receive notifications. Share the webhook path/URL (POST /aog-parts-release) with the upstream system sending requests and align the payload fields (for example parts[].partNumber and parts[].serialNumber) to match your source data. Requirements n8n Instance: Compatible with self-hosted (npm/Docker) or n8n Cloud (v1.0+) OpenAI API Key: Account with access to chat models (default: gpt-5-mini) for structured entity extraction and drafting Google OAuth2 Credentials: Connected accounts for Gmail (intake/notifications), Google Drive (evidence storage), and Google Sheets (audit logging) HTTP/Header Credentials: API keys for external PRO work registration and Master rights claim endpoints (or compatible internal rights registries) Customization Risk Threshold Logic: Adjust tolerance parameters inside the Calculate Clearance Gaps node to enforce custom rules on writer share splits, missing sync quotes, or territory restrictions. Notification & Approval Channels: Replace the supervisor Gmail notification and approval webhook with Slack, Microsoft Teams, or Discord alerts. Extraction Schema & Prompts: Modify the schema in Extract Music Cue Rights and Draft Rights Clearance Packet to capture specialized fields (e.g., ISRC codes, ISWC, custom episode codes, or custom licensing fee bands). Downstream Production Storage: Re-route the final approved HTTP POST and archive sinks to internal production ERPs, Airtable, Notion, or custom SQL databases. Additional info Production-Grade Resilience: All external touchpoints (OpenAI, Gmail, Google APIs, and HTTP registries) come configured with automatic 3x retries and a 2-second backoff to handle transient network blips. Deterministic Safety-First Design: Employs a strict human-in-the-loop wait-webhook so unverified rights claims are never committed to production without explicit supervisor approval. Embedded Canvas Documentation: Includes 4 comprehensive master documentation stickies detailing the complete node map, setup protocol, credential checklists, and privacy considerations. Audit-Ready Logging: Automatically exports raw clearance evidence as JSON into Google Drive while creating an immutable append-only ledger record in Google Sheets. Estimated Implementation Time: 25–40 minutes to configure credential nodes, folder IDs, and target webhook URLs.

This workflow demonstrates two ways of exporting data from SQL to XML. First, several random records are received from the MySQL database. Then, in the upper part of the workflow, the structure of an XML is defined in the Set node. After that, the ItemLists node combines all items into an array. This allows an XML node to create a simple XML file. The lower part of the workflow shows how to create an XML with attributes. It is almost identical except that a $ (dollar sign) JSON key is used to define XML attributes. Finally, both files are saved locally.

Wan Video Wan 2.2 I2v A14b Video Generator Description Image-to-video at 720p and 480p with Wan 2.2 A14B Overview This n8n workflow integrates with the Replicate API to use the wan-video/wan-2.2-i2v-a14b model. This powerful AI model can generate high-quality video content based on your inputs. Features Easy integration with Replicate API Automated status checking and result retrieval Support for all model parameters Error handling and retry logic Clean output formatting Parameters Required Parameters prompt** (string): Prompt for video generation image** (string): Input image to generate video from Optional Parameters seed** (integer, default: None): Random seed. Leave blank for random num_frames** (integer, default: 81): Number of video frames. 81 frames give the best results resolution** (string, default: 480p): Resolution of video. 832x480px corresponds to 16:9 aspect ratio, and 480x832px is 9:16 sample_shift** (number, default: 5): Sample shift factor sample_steps** (integer, default: 30): Number of generation steps. Fewer steps means faster generation, at the expensive of output quality. 30 steps is sufficient for most prompts frames_per_second** (integer, default: 16): Frames per second. Note that the pricing of this model is based on the video duration at 16 fps How to Use Set up your Replicate API key in the workflow Configure the required parameters for your use case Run the workflow to generate video content Access the generated output from the final node API Reference Model: wan-video/wan-2.2-i2v-a14b API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of video generation parameters

Fire Flux Image Generator Description The image generation model tailored for local development and personal use Overview This n8n workflow integrates with the Replicate API to use the fire/flux model. This powerful AI model can generate high-quality image content based on your inputs. Features Easy integration with Replicate API Automated status checking and result retrieval Support for all model parameters Error handling and retry logic Clean output formatting Parameters Required Parameters prompt** (string): Prompt for generated image Optional Parameters seed** (integer, default: 0): Random seed. Set for reproducible generation go_fast** (boolean, default: True): Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16 megapixels** (string, default: 1): Approximate number of megapixels for generated image num_outputs** (integer, default: 1): Number of outputs to generate aspect_ratio** (string, default: 2:1): Aspect ratio for the generated image output_format** (string, default: png): Format of the output images output_quality** (integer, default: 80): Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs num_inference_steps** (integer, default: 4): Number of denoising steps. 4 is recommended, and lower number of steps produce lower quality outputs, faster. disable_safety_checker** (boolean, default: False): Disable safety checker for generated images. How to Use Set up your Replicate API key in the workflow Configure the required parameters for your use case Run the workflow to generate image content Access the generated output from the final node API Reference Model: fire/flux API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of image generation parameters